Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations36520
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 MiB
Average record size in memory264.2 B

Variable types

DateTime1
Categorical3
Numeric8

Alerts

Humidity is highly overall correlated with TemperatureHigh correlation
K is highly overall correlated with N and 3 other fieldsHigh correlation
N is highly overall correlated with K and 3 other fieldsHigh correlation
P is highly overall correlated with K and 3 other fieldsHigh correlation
Soil_Quality is highly overall correlated with Soil_Type and 1 other fieldsHigh correlation
Soil_Type is highly overall correlated with K and 4 other fieldsHigh correlation
Soil_pH is highly overall correlated with K and 4 other fieldsHigh correlation
Temperature is highly overall correlated with HumidityHigh correlation
Crop_Type is uniformly distributed Uniform
Temperature has unique values Unique
Wind_Speed has unique values Unique
Crop_Yield has 11025 (30.2%) zeros Zeros

Reproduction

Analysis started2025-09-17 01:05:04.296783
Analysis finished2025-09-17 01:05:10.191490
Duration5.89 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Date
Date

Distinct3652
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size285.4 KiB
Minimum2014-01-01 00:00:00
Maximum2023-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-16T20:05:10.310751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:10.418463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Crop_Type
Categorical

Uniform 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Wheat
3652 
Corn
3652 
Rice
3652 
Barley
3652 
Soybean
3652 
Other values (5)
18260 

Length

Max length9
Median length7
Mean length6.2
Min length4

Characters and Unicode

Total characters226424
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWheat
2nd rowCorn
3rd rowRice
4th rowBarley
5th rowSoybean

Common Values

ValueCountFrequency (%)
Wheat 3652
10.0%
Corn 3652
10.0%
Rice 3652
10.0%
Barley 3652
10.0%
Soybean 3652
10.0%
Cotton 3652
10.0%
Sugarcane 3652
10.0%
Tomato 3652
10.0%
Potato 3652
10.0%
Sunflower 3652
10.0%

Length

2025-09-16T20:05:10.517198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T20:05:10.605601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
wheat 3652
10.0%
corn 3652
10.0%
rice 3652
10.0%
barley 3652
10.0%
soybean 3652
10.0%
cotton 3652
10.0%
sugarcane 3652
10.0%
tomato 3652
10.0%
potato 3652
10.0%
sunflower 3652
10.0%

Most occurring characters

ValueCountFrequency (%)
o 32868
14.5%
a 25564
11.3%
t 21912
 
9.7%
e 21912
 
9.7%
n 18260
 
8.1%
r 14608
 
6.5%
S 10956
 
4.8%
C 7304
 
3.2%
y 7304
 
3.2%
u 7304
 
3.2%
Other values (14) 58432
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 226424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 32868
14.5%
a 25564
11.3%
t 21912
 
9.7%
e 21912
 
9.7%
n 18260
 
8.1%
r 14608
 
6.5%
S 10956
 
4.8%
C 7304
 
3.2%
y 7304
 
3.2%
u 7304
 
3.2%
Other values (14) 58432
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 226424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 32868
14.5%
a 25564
11.3%
t 21912
 
9.7%
e 21912
 
9.7%
n 18260
 
8.1%
r 14608
 
6.5%
S 10956
 
4.8%
C 7304
 
3.2%
y 7304
 
3.2%
u 7304
 
3.2%
Other values (14) 58432
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 226424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 32868
14.5%
a 25564
11.3%
t 21912
 
9.7%
e 21912
 
9.7%
n 18260
 
8.1%
r 14608
 
6.5%
S 10956
 
4.8%
C 7304
 
3.2%
y 7304
 
3.2%
u 7304
 
3.2%
Other values (14) 58432
25.8%

Soil_Type
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Saline
7361 
Clay
7300 
Loamy
7288 
Peaty
7286 
Sandy
7285 

Length

Max length6
Median length5
Mean length5.0016703
Min length4

Characters and Unicode

Total characters182661
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPeaty
2nd rowLoamy
3rd rowPeaty
4th rowSandy
5th rowPeaty

Common Values

ValueCountFrequency (%)
Saline 7361
20.2%
Clay 7300
20.0%
Loamy 7288
20.0%
Peaty 7286
20.0%
Sandy 7285
19.9%

Length

2025-09-16T20:05:10.725808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T20:05:10.787642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
saline 7361
20.2%
clay 7300
20.0%
loamy 7288
20.0%
peaty 7286
20.0%
sandy 7285
19.9%

Most occurring characters

ValueCountFrequency (%)
a 36520
20.0%
y 29159
16.0%
l 14661
8.0%
e 14647
8.0%
n 14646
8.0%
S 14646
8.0%
i 7361
 
4.0%
C 7300
 
4.0%
L 7288
 
4.0%
o 7288
 
4.0%
Other values (4) 29145
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 36520
20.0%
y 29159
16.0%
l 14661
8.0%
e 14647
8.0%
n 14646
8.0%
S 14646
8.0%
i 7361
 
4.0%
C 7300
 
4.0%
L 7288
 
4.0%
o 7288
 
4.0%
Other values (4) 29145
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 36520
20.0%
y 29159
16.0%
l 14661
8.0%
e 14647
8.0%
n 14646
8.0%
S 14646
8.0%
i 7361
 
4.0%
C 7300
 
4.0%
L 7288
 
4.0%
o 7288
 
4.0%
Other values (4) 29145
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 36520
20.0%
y 29159
16.0%
l 14661
8.0%
e 14647
8.0%
n 14646
8.0%
S 14646
8.0%
i 7361
 
4.0%
C 7300
 
4.0%
L 7288
 
4.0%
o 7288
 
4.0%
Other values (4) 29145
16.0%

Soil_pH
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
8.0
7361 
6.25
7300 
6.5
7288 
5.5
7286 
6.75
7285 

Length

Max length4
Median length3
Mean length3.3993702
Min length3

Characters and Unicode

Total characters124145
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.5
2nd row6.5
3rd row5.5
4th row6.75
5th row5.5

Common Values

ValueCountFrequency (%)
8.0 7361
20.2%
6.25 7300
20.0%
6.5 7288
20.0%
5.5 7286
20.0%
6.75 7285
19.9%

Length

2025-09-16T20:05:10.868426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T20:05:10.922283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8.0 7361
20.2%
6.25 7300
20.0%
6.5 7288
20.0%
5.5 7286
20.0%
6.75 7285
19.9%

Most occurring characters

ValueCountFrequency (%)
. 36520
29.4%
5 36445
29.4%
6 21873
17.6%
0 7361
 
5.9%
8 7361
 
5.9%
2 7300
 
5.9%
7 7285
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 124145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 36520
29.4%
5 36445
29.4%
6 21873
17.6%
0 7361
 
5.9%
8 7361
 
5.9%
2 7300
 
5.9%
7 7285
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 124145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 36520
29.4%
5 36445
29.4%
6 21873
17.6%
0 7361
 
5.9%
8 7361
 
5.9%
2 7300
 
5.9%
7 7285
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 124145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 36520
29.4%
5 36445
29.4%
6 21873
17.6%
0 7361
 
5.9%
8 7361
 
5.9%
2 7300
 
5.9%
7 7285
 
5.9%

Temperature
Real number (ℝ)

High correlation  Unique 

Distinct36520
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.813996
Minimum-3.5401758
Maximum54.148911
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)< 0.1%
Memory size285.4 KiB
2025-09-16T20:05:11.090803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.5401758
5-th percentile10.36247
Q117.168542
median22.902987
Q330.254748
95-th percentile39.283578
Maximum54.148911
Range57.689087
Interquartile range (IQR)13.086206

Descriptive statistics

Standard deviation8.9205187
Coefficient of variation (CV)0.37459142
Kurtosis-0.54206661
Mean23.813996
Median Absolute Deviation (MAD)6.4005242
Skewness0.2471353
Sum869687.15
Variance79.575653
MonotonicityNot monotonic
2025-09-16T20:05:11.184580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.45569221 1
 
< 0.1%
9.44059941 1
 
< 0.1%
20.05257642 1
 
< 0.1%
12.14309917 1
 
< 0.1%
19.75184841 1
 
< 0.1%
16.11039451 1
 
< 0.1%
14.82673908 1
 
< 0.1%
13.53300427 1
 
< 0.1%
11.03416914 1
 
< 0.1%
9.62973959 1
 
< 0.1%
Other values (36510) 36510
> 99.9%
ValueCountFrequency (%)
-3.54017583 1
< 0.1%
-2.390846068 1
< 0.1%
-2.266770416 1
< 0.1%
-2.240214571 1
< 0.1%
-2.216595759 1
< 0.1%
-1.877794216 1
< 0.1%
-1.237901502 1
< 0.1%
-1.126530945 1
< 0.1%
-0.6522678112 1
< 0.1%
-0.6107232672 1
< 0.1%
ValueCountFrequency (%)
54.14891086 1
< 0.1%
53.44738987 1
< 0.1%
53.05675031 1
< 0.1%
52.04025353 1
< 0.1%
52.0260266 1
< 0.1%
51.73501561 1
< 0.1%
51.67634095 1
< 0.1%
50.78385913 1
< 0.1%
50.58840565 1
< 0.1%
50.53234811 1
< 0.1%

Humidity
Real number (ℝ)

High correlation 

Distinct22906
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.256624
Minimum45.851089
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.4 KiB
2025-09-16T20:05:11.275311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum45.851089
5-th percentile60.716422
Q169.745252
median77.097013
Q380
95-th percentile80
Maximum80
Range34.148911
Interquartile range (IQR)10.254748

Descriptive statistics

Standard deviation6.7675874
Coefficient of variation (CV)0.091137828
Kurtosis-0.02446876
Mean74.256624
Median Absolute Deviation (MAD)2.9029867
Skewness-1.0171689
Sum2711851.9
Variance45.800239
MonotonicityNot monotonic
2025-09-16T20:05:11.377067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 13615
37.3%
78.84110812 1
 
< 0.1%
77.97397347 1
 
< 0.1%
73.63327421 1
 
< 0.1%
71.39915417 1
 
< 0.1%
73.42670717 1
 
< 0.1%
78.39596636 1
 
< 0.1%
78.88898327 1
 
< 0.1%
79.98186192 1
 
< 0.1%
71.20773401 1
 
< 0.1%
Other values (22896) 22896
62.7%
ValueCountFrequency (%)
45.85108914 1
< 0.1%
46.55261013 1
< 0.1%
46.94324969 1
< 0.1%
47.95974647 1
< 0.1%
47.9739734 1
< 0.1%
48.26498439 1
< 0.1%
48.32365905 1
< 0.1%
49.21614087 1
< 0.1%
49.41159435 1
< 0.1%
49.46765189 1
< 0.1%
ValueCountFrequency (%)
80 13615
37.3%
79.99938941 1
 
< 0.1%
79.9991059 1
 
< 0.1%
79.99870104 1
 
< 0.1%
79.99791024 1
 
< 0.1%
79.99747049 1
 
< 0.1%
79.99741256 1
 
< 0.1%
79.99508767 1
 
< 0.1%
79.99484521 1
 
< 0.1%
79.99476954 1
 
< 0.1%

Wind_Speed
Real number (ℝ)

Unique 

Distinct36520
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.020153
Minimum-3.3889056
Maximum22.606078
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)< 0.1%
Memory size285.4 KiB
2025-09-16T20:05:11.478766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.3889056
5-th percentile5.1116939
Q17.9858716
median10.000299
Q312.038546
95-th percentile14.984975
Maximum22.606078
Range25.994983
Interquartile range (IQR)4.0526748

Descriptive statistics

Standard deviation2.9983098
Coefficient of variation (CV)0.29922795
Kurtosis-0.031019812
Mean10.020153
Median Absolute Deviation (MAD)2.0278154
Skewness0.016443919
Sum365935.99
Variance8.9898616
MonotonicityNot monotonic
2025-09-16T20:05:11.571517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.394757813 1
 
< 0.1%
10.95670655 1
 
< 0.1%
8.591576842 1
 
< 0.1%
7.227751487 1
 
< 0.1%
2.682682555 1
 
< 0.1%
7.696070494 1
 
< 0.1%
10.3666575 1
 
< 0.1%
9.910484292 1
 
< 0.1%
4.558410814 1
 
< 0.1%
9.134019127 1
 
< 0.1%
Other values (36510) 36510
> 99.9%
ValueCountFrequency (%)
-3.388905581 1
< 0.1%
-1.884738704 1
< 0.1%
-1.157605916 1
< 0.1%
-1.131445071 1
< 0.1%
-1.110924283 1
< 0.1%
-1.089865717 1
< 0.1%
-0.8946164418 1
< 0.1%
-0.6574369754 1
< 0.1%
-0.622883439 1
< 0.1%
-0.5997617039 1
< 0.1%
ValueCountFrequency (%)
22.6060777 1
< 0.1%
22.30102682 1
< 0.1%
21.70839615 1
< 0.1%
21.11572964 1
< 0.1%
20.80849641 1
< 0.1%
20.79943511 1
< 0.1%
20.66685139 1
< 0.1%
20.55390211 1
< 0.1%
20.19438195 1
< 0.1%
20.08084993 1
< 0.1%

N
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.011035
Minimum45
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.4 KiB
2025-09-16T20:05:11.648340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile50
Q158.5
median65
Q371.5
95-th percentile84.5
Maximum91
Range46
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.886721
Coefficient of variation (CV)0.16492274
Kurtosis-0.44535584
Mean66.011035
Median Absolute Deviation (MAD)6.5
Skewness0.33352302
Sum2410723
Variance118.52069
MonotonicityNot monotonic
2025-09-16T20:05:11.720122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
71.5 3712
 
10.2%
65 3545
 
9.7%
60 2911
 
8.0%
66 2907
 
8.0%
70 2267
 
6.2%
78 2257
 
6.2%
50 2225
 
6.1%
60.5 2195
 
6.0%
55 2189
 
6.0%
77 2161
 
5.9%
Other values (10) 10151
27.8%
ValueCountFrequency (%)
45 728
 
2.0%
49.5 732
 
2.0%
50 2225
6.1%
54 711
 
1.9%
55 2151
5.9%
55 2189
6.0%
58.5 759
 
2.1%
60 2911
8.0%
60.5 2195
6.0%
63 722
 
2.0%
ValueCountFrequency (%)
91 1400
 
3.8%
84.5 1496
4.1%
84 738
 
2.0%
78 2257
6.2%
77 2161
5.9%
72 714
 
2.0%
71.5 3712
10.2%
70 2267
6.2%
66 2907
8.0%
65 3545
9.7%

P
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.014006
Minimum36
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.4 KiB
2025-09-16T20:05:11.783951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile40
Q145
median54
Q360
95-th percentile66
Maximum72
Range36
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8128843
Coefficient of variation (CV)0.1662369
Kurtosis-0.722128
Mean53.014006
Median Absolute Deviation (MAD)6
Skewness0.19313816
Sum1936071.5
Variance77.666929
MonotonicityNot monotonic
2025-09-16T20:05:11.856786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
60 4426
12.1%
66 3748
10.3%
45 3693
10.1%
40 2953
8.1%
55 2923
8.0%
50 2874
7.9%
60.5 2211
 
6.1%
54 2207
 
6.0%
44 2174
 
6.0%
55 2165
 
5.9%
Other values (6) 7146
19.6%
ValueCountFrequency (%)
36 747
 
2.0%
40 2953
8.1%
40.5 703
 
1.9%
44 2174
6.0%
45 3693
10.1%
48 1411
 
3.9%
49.5 731
 
2.0%
49.5 2154
5.9%
50 2874
7.9%
54 2207
6.0%
ValueCountFrequency (%)
72 1400
 
3.8%
66 3748
10.3%
60.5 2211
6.1%
60 4426
12.1%
55 2165
5.9%
55 2923
8.0%
54 2207
6.0%
50 2874
7.9%
49.5 2154
5.9%
49.5 731
 
2.0%

K
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.01825
Minimum27
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.4 KiB
2025-09-16T20:05:11.930585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile30
Q135
median42
Q349.5
95-th percentile55
Maximum60
Range33
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.5378105
Coefficient of variation (CV)0.20319291
Kurtosis-0.79392391
Mean42.01825
Median Absolute Deviation (MAD)7
Skewness0.20620103
Sum1534506.5
Variance72.894208
MonotonicityNot monotonic
2025-09-16T20:05:12.002394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
45 3575
 
9.8%
36 2860
 
7.8%
50 2246
 
6.2%
30 2224
 
6.1%
49.5 2206
 
6.0%
44 2204
 
6.0%
38.5 2196
 
6.0%
33 2182
 
6.0%
40 2168
 
5.9%
55 2168
 
5.9%
Other values (8) 12491
34.2%
ValueCountFrequency (%)
27 1480
4.1%
30 2224
6.1%
31.5 1483
4.1%
33 2182
6.0%
35 2149
5.9%
36 2860
7.8%
38.5 2196
6.0%
40 2168
5.9%
40.5 1474
4.0%
42 1458
4.0%
ValueCountFrequency (%)
60 1468
4.0%
55 2168
5.9%
54 1512
4.1%
50 2246
6.2%
49.5 2206
6.0%
48 1467
4.0%
45 3575
9.8%
44 2204
6.0%
42 1458
4.0%
40.5 1474
4.0%

Crop_Yield
Real number (ℝ)

Zeros 

Distinct25496
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.87848
Minimum0
Maximum136.71198
Zeros11025
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size285.4 KiB
2025-09-16T20:05:12.085171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23.366344
Q346.415729
95-th percentile72.819665
Maximum136.71198
Range136.71198
Interquartile range (IQR)46.415729

Descriptive statistics

Standard deviation25.740936
Coefficient of variation (CV)0.95767828
Kurtosis-0.48188632
Mean26.87848
Median Absolute Deviation (MAD)23.366344
Skewness0.62802583
Sum981602.08
Variance662.5958
MonotonicityNot monotonic
2025-09-16T20:05:12.185902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11025
30.2%
104.8713103 1
 
< 0.1%
58.93979634 1
 
< 0.1%
32.97041272 1
 
< 0.1%
29.35611549 1
 
< 0.1%
22.22137511 1
 
< 0.1%
54.25972686 1
 
< 0.1%
68.11924993 1
 
< 0.1%
28.72353012 1
 
< 0.1%
37.13220059 1
 
< 0.1%
Other values (25486) 25486
69.8%
ValueCountFrequency (%)
0 11025
30.2%
0.001761170836 1
 
< 0.1%
0.004505416905 1
 
< 0.1%
0.008532342327 1
 
< 0.1%
0.01014890965 1
 
< 0.1%
0.01293884402 1
 
< 0.1%
0.01572844942 1
 
< 0.1%
0.01574269673 1
 
< 0.1%
0.0183969908 1
 
< 0.1%
0.02501011998 1
 
< 0.1%
ValueCountFrequency (%)
136.7119817 1
< 0.1%
136.3337676 1
< 0.1%
133.0855228 1
< 0.1%
131.9599111 1
< 0.1%
125.681375 1
< 0.1%
124.9603095 1
< 0.1%
124.6182287 1
< 0.1%
123.8670563 1
< 0.1%
123.2707436 1
< 0.1%
121.6923543 1
< 0.1%

Soil_Quality
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.516632
Minimum13.291667
Maximum74.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.4 KiB
2025-09-16T20:05:12.279651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.291667
5-th percentile13.75
Q122.5
median35.583333
Q349.291667
95-th percentile67.666667
Maximum74.333333
Range61.041667
Interquartile range (IQR)26.791667

Descriptive statistics

Standard deviation17.703171
Coefficient of variation (CV)0.47187528
Kurtosis-0.99963003
Mean37.516632
Median Absolute Deviation (MAD)13.416667
Skewness0.30675974
Sum1370107.4
Variance313.40227
MonotonicityNot monotonic
2025-09-16T20:05:12.370409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
22.83333333 1483
 
4.1%
35.58333333 1480
 
4.1%
13.91666667 1474
 
4.0%
44.33333333 1461
 
4.0%
60.66666667 1406
 
3.8%
60 789
 
2.2%
53.08333333 768
 
2.1%
17.04166667 762
 
2.1%
13.58333333 759
 
2.1%
63.66666667 759
 
2.1%
Other values (35) 25379
69.5%
ValueCountFrequency (%)
13.29166667 731
2.0%
13.58333333 759
2.1%
13.75 690
1.9%
13.91666667 1474
4.0%
14.58333333 713
2.0%
15.29166667 748
2.0%
15.5 750
2.1%
16.66666667 734
2.0%
17.04166667 762
2.1%
21.75 703
1.9%
ValueCountFrequency (%)
74.33333333 713
2.0%
72.66666667 687
1.9%
67.66666667 755
2.1%
66.66666667 738
2.0%
63.66666667 759
2.1%
60.66666667 1406
3.8%
60 789
2.2%
59.33333333 722
2.0%
58 719
2.0%
54.25 741
2.0%

Interactions

2025-09-16T20:05:09.327742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:04.995327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.609712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.240996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.840394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.404884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.095038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.696431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.398551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.077108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.688473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.310838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.911203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.479711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.167872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.771257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.496315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.157893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.769259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.387604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.984010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.563487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.245663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.854035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.570093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.231695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.845056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.463402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.051828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.640254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.320436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.930804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.645918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.304501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.920852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.542190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.114687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.713061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.392243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.007597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.722712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.384287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.001636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.619016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.188462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.791849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.469038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.089405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.797513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.459088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.081424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.693817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.258276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.942446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.544835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.171163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.878270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:05.538874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.161210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:06.770610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:07.334073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.020238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:08.623625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-16T20:05:09.250947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-16T20:05:12.444184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Crop_TypeCrop_YieldHumidityKNPSoil_QualitySoil_TypeSoil_pHTemperatureWind_Speed
Crop_Type1.0000.0440.0000.3170.3650.3160.3340.0000.0000.0000.004
Crop_Yield0.0441.0000.4200.1070.0940.1120.1350.1950.195-0.306-0.009
Humidity0.0000.4201.000-0.002-0.003-0.0010.0060.0000.000-0.974-0.002
K0.3170.107-0.0021.0000.6690.9080.2860.6160.6160.002-0.001
N0.3650.094-0.0030.6691.0000.8450.2960.5510.5510.002-0.003
P0.3160.112-0.0010.9080.8451.0000.3110.5860.5860.001-0.000
Soil_Quality0.3340.1350.0060.2860.2960.3111.0000.9570.957-0.007-0.007
Soil_Type0.0000.1950.0000.6160.5510.5860.9571.0001.0000.0000.000
Soil_pH0.0000.1950.0000.6160.5510.5860.9571.0001.0000.0000.000
Temperature0.000-0.306-0.9740.0020.0020.001-0.0070.0000.0001.0000.001
Wind_Speed0.004-0.009-0.002-0.001-0.003-0.000-0.0070.0000.0000.0011.000

Missing values

2025-09-16T20:05:09.999975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-16T20:05:10.102728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateCrop_TypeSoil_TypeSoil_pHTemperatureHumidityWind_SpeedNPKCrop_YieldSoil_Quality
02014-01-01WheatPeaty5.509.44059980.00000010.95670760.545.031.50.00000022.833333
12014-01-01CornLoamy6.5020.05257679.9474248.59157784.066.050.0104.87131066.666667
22014-01-01RicePeaty5.5012.14309980.0000007.22775171.554.038.50.00000027.333333
32014-01-01BarleySandy6.7519.75184880.0000002.68268350.040.030.058.93979635.000000
42014-01-01SoybeanPeaty5.5016.11039580.0000007.69607049.545.038.532.97041322.166667
52014-01-01CottonSandy6.7514.82673980.00000010.36665755.044.036.029.35611539.375000
62014-01-01SugarcaneSaline8.0013.53300480.0000009.91048484.566.054.00.00000017.041667
72014-01-01TomatoClay6.2518.32327280.0000008.19808460.045.040.022.22137542.291667
82014-01-01PotatoPeaty5.5018.89596380.0000006.69670760.545.031.554.25972722.833333
92014-01-01SunflowerLoamy6.5016.86559580.0000009.31164970.066.055.068.11925063.666667
DateCrop_TypeSoil_TypeSoil_pHTemperatureHumidityWind_SpeedNPKCrop_YieldSoil_Quality
365102023-12-31WheatPeaty5.504.33414380.0000007.45068560.545.031.50.00000022.833333
365112023-12-31CornSaline8.0013.07963580.00000016.02793678.060.545.020.73513515.291667
365122023-12-31RiceSandy6.753.99462880.00000014.38834165.048.033.00.00000042.583333
365132023-12-31BarleyClay6.2511.61777180.00000014.70048560.050.040.013.06916943.750000
365142023-12-31SoybeanLoamy6.507.28210680.00000015.26194563.060.055.00.00000059.333333
365152023-12-31CottonClay6.2519.53855580.0000003.66666466.055.048.073.32388549.291667
365162023-12-31SugarcanePeaty5.5021.06833678.9316648.79503671.554.042.039.22652127.916667
365172023-12-31TomatoSandy6.756.03014880.0000009.40949750.036.030.00.00000033.833333
365182023-12-31PotatoPeaty5.5011.07956180.00000010.96936660.545.031.56.06788122.833333
365192023-12-31SunflowerClay6.2511.45569280.0000005.39475860.055.044.011.82598646.375000